JOINT RESEARCH CENTER FOR PANEL STUDIESDISCUSSION PAPER SERIES

DP2009-001
August, 2009

Earthquake Risk and Housing Prices in Japan:
Evidence Beforeand After Massive Earthquakes

Michio Naoi*
Miki Seko**Kazuto Sumita***
Abstract
The hedonic pricing approach is used to examine whether homeowners and/or rentersalter their subjective assessments of earthquake risks after massive earthquakes. Using
nation-wide household panel data coupled with earthquake hazard information andrecords of observed earthquakes, we find that there are some modifications of individuals’
assessments of earthquake risk in both cases. We have carefully taken into consideration
the bias stemming from the use of objective risk variables as a proxy for individual risk
assessments. Our results suggest that the price discount from locating within aquake-prone area is significantly larger soon after earthquake events than beforehand. We
argue that the most likely interpretation for this result is that households tend tounderestimate earthquake risk if there has not been a recent occurrence.

*Michio Naoi
Faculty of Economics, Keio University** Miki Seko

Faculty of Economics, Keio University*** Kazuto Sumita

Department of Economics, Kanazawa Seiryo University

Joint Research Center for Panel Studies
Keio University

Earthquake Risk and Housing Prices in Japan: Evidence Before
and After Massive Earthquakes*

First draft: May28, 2008
First revision: June 29, 2009

Michio Naoia, Miki Sekob†, Kazuto Sumitac

a. Faculty of Economics, Keio University, Mita Toho Bldg. 3rd Floor, 3-1-7 Mita,
Minato-ku, Tokyo, 108-0073, Japanb. Faculty of Economics, Keio University, 2-15-45 Mita, Minato-ku, Tokyo, 108-8345,
Japan
c. Department of Economics, Kanazawa Seiryo University, Ushi 10-1, Gosho-machi,
Kanazawa-shi, Ishikawa, 920-8620, Japan*We are grateful to the National Research Institute for Earth Science and Disaster Prevention (NIED) for

generously providing us with the data on earthquake hazard information. We also thank Richard Arnott,
Eric Hanushek, Dwight Jaffee, Charles Leung, Colin McKenzie, Tsunao Okumura, John Quigley, Tim
Riddiough, James Shilling, Jay Weiser, Jiro Yoshida, two anonymous referees and participants at theMacro, Real Estate and Public Policy Workshop, the 12th AsRES Annual Conference and the International
Conference on Economic Analysis and Policy Evaluation Using Panel Data for their helpful comments.
Financial support from the Japan Economic Research Foundation is gratefully acknowledged. The firstauthor (Michio Naoi) acknowledges a Grant-in-Aid (#19730183) for Young Scientists from the Ministryof Education, Culture, Sports, Science and Technology. The second and third authors (Miki Seko andKazuto Sumita) acknowledge a Grant-in-Aid (#19530157) for Scientific Research (C) from the Ministryof Education Culture, Sports, Science and Technology.
†

Corresponding Author. E-mail address: seko@econ.keio.ac.jp1Abstract
The hedonic pricing approach is used to examine whether homeowners and/or rentersalter their subjective assessments of earthquake risks after massive earthquakes. Using
nation-wide household panel data coupled with earthquake hazard information andrecords of observed earthquakes, we find that there are some modifications of individuals’
assessments of earthquake risk in both cases. We have carefully taken into consideration
the bias stemming from the use of objective risk variables as a proxy for individual risk
assessments. Our results suggest that the price discount from locating within aquake-prone area is significantly larger soon after earthquake events than beforehand. We
argue that the most likely interpretation for this result is that households tend tounderestimate earthquake risk if there has not been a recent occurrence.

JEL classification: R20, C23.
Keywords: Earthquake risks, Hedonic price model, Panel data models.

2

1. Introduction
Japan is one of the world’s most earthquake-prone countries since it lies at the nexus of four
tectonic plates. A recent survey reports that Japan averaged 1.14 earthquake events equal to or
greater than a magnitude of 5.5 on the Richter scale a year between 1980 and 2000, whichaccording to the United Nations Development Program is fourth highest among 50 countries
surveyed (UNDP, 2004). It is assumed that these massive earthquakes increase awareness ofearthquake risk among individuals, largely due to extensive media coverage of the event and theresulting quake damages. Indeed, sales of earthquake insurance policies increased by 75% in
Hyogo Prefecture in 1995, immediately after the Great Hanshin-Awaji (Kobe) Earthquake, andby nearly 25% in Miyagi Prefecture after the 2003 Miyagi Earthquake, while the corresponding
nation-wide increases were only 29% and 5% respectively.
If existing government anti-seismic policies are efficient in the sense that earthquake risks
are properly assessed and individuals are well-informed about these risks, major tremors should
not alter individuals’ perception toward risk. Therefore, modifications of individuals’
perceptions after an earthquake indicates that there is room for improvement in the current
anti-seismic policies concerning risk assessment and its dissemination among the public.
In this paper, we use a hedonic pricing approach to estimate individuals’ valuation ofearthquake risk. The nation-wide household longitudinal data coupled with earthquake hazard
information and the observed earthquake record allows us to investigate whether individuals,i.e., homeowners and renters, alter their subjective assessments of earthquake risks after anearthquake. Using a difference-in-differences (DID) approach with longitudinal data, we findthat there are some post-quake modifications of individuals’ assessments of earthquake risk. Our
results indicate that, while there is no clear evidence about pre-quake responses to earthquake3risk, both housing rents and owner-occupied home values are significantly and negatively
correlated with regional earthquake risk in post-quake periods. The most plausible interpretation
for these results is that both renters and homeowners are initially unaware of, or at least
underestimate, earthquake risk. We also conduct several robustness checks on our main
empirical findings, and compare the homeowner’s implicit price estimates with capitalized
insurance premiums.
The paper is organized as follows. Section 2 briefly reviews the previous studies ofearthquake risk in the housing market. Section 3 presents a simple theoretical framework.
Section 4 introduces the data used and explains estimation methods and variables. Section 5
presents empirical results and interpretation. Section 6 summarizes the paper and presents someconclusions.

2. Previous Studies
Since an earthquake is an exogenous risk factor that is tied to a specific location, its risk should
be capitalized into local housing and land prices. Estimating individuals’ valuation ofearthquake risk is crucial for evaluating the benefits of earthquake damage mitigation policies.
There have been relatively few studies on the effect of earthquake risk on housing and land
prices despite the obvious relevance to effective disaster prevention policies. Brookshire, Thayer,
Tschirhart, and Schulze (1985) examine the effects of the disclosure of a risk hazard map in
California on sales prices of single-family houses. It is found that the earthquake hazard indicesdo have a significantly negative impact on prices after they are disclosed. Nakagawa, Saito, andYamaga (2009) empirically investigate the effect of earthquake risk on land prices using theearthquake risk index taken from the earthquake hazard map compiled by the Tokyo

4

Metropolitan Government. Their results suggest that higher earthquake risk is clearly related tolower land prices in each area. Nakagawa, Saito, and Yamaga (2007) examine the impact ofearthquake risk on housing rents in the Tokyo Metropolitan Area with special reference to the
new Building Standard Law enacted in 1981, using the same earthquake risk index used by
Nakagawa et al. (2009). They find that housing rents are substantially lower in the areas ofhigher earthquake risk. Also, they find that the rent of houses built prior to 1981 is discounted
more substantially in risky areas than for houses built after 1981.
Beron, Murdoch, Thayer, and Vijverberg (1997) conduct hedonic analysis of residential
housing prices in the San Francisco Bay area using the expected loss from earthquakes as anadditional explanatory variable, and compare the estimated hedonic functions before and after
the 1989 Loma Prieta Earthquake. 1 The result indicates that the hazard indices have asignificantly negative impact on housing prices in both time periods; however, its impact isgreater in the pre-earthquake period, implying that earthquake risk was overestimated before theLoma Prieta Earthquake occurred. Significantly, they focus only on the area actually hit by theearthquake, and their results are obtained by simply comparing before and after the earthquake
without any relevant control group. Thus, their results may reflect some other factors that render
earthquake risk less important over time such as improvement in quake-resistant construction
technology.
Compared with previous studies, our contributions are as follows. First, while previous
studies mainly focus on small areas that are subject to a specific earthquake event, we use
1

There are also several studies about the relationship between flood hazards and housing prices in the

US. Among others, Bin and Polasky (2004) and Hallstrom and Smith (2005) investigate the effect ofspecific hurricane events – Floyd and Andrew – on individuals’ perception of flood risks. Shilling,
Benjamin and Sirmans (1987), MacDonald, Murdoch, and White (1987), MacDonald, White, Taube, andHuth (1990) and Bin, Kruse, and Landry (2008) have also evaluated the effect of flood hazard on housing
values.

5

nation-wide longitudinal data that covers areas with near-miss incidents of massive earthquakes,
i.e. whose neighboring city/towns were recently hit by a massive earthquake, as well as those
without any such experiences. Since housing prices are influenced by various factors varying
over time, a simple before-after comparison within a small area might yield misleading
conclusions. Second, we use longitudinal data rather than (repeated) cross-section data in our
analysis. By observing the same households repeatedly, we can control for individual
heterogeneity that influences housing price changes over time. Further, we carefully examine
the bias stemming from the use of objective risk variables as a proxy for subjective assessments,
utilizing the panel data instrumental variables estimator. Third, we examine whether
homeowners and renters alter their subjective assessments of earthquake risks after massive
earthquakes. To the best of our knowledge, there has been no prior attempt to analyze the effectsof earthquake risk on property values before and after a major earthquake event for both
homeowners and renters.

3. Theoretical Considerations
Hedonic pricing model with uncertain hazardous events
In this section, we present a simple hedonic pricing model to illustrate household responses tothe prospect of hazardous events and the effect of additional information on perceptions towardrisk. A similar model is also presented in MacDonald et al. (1987) and Kask and Maani (1992).
The basic intuition of the model is that household’s valuation of the different bundles of housing
and location characteristics leads to differential housing costs. Thus, the observed discount on
property in a hazardous area reflects households’ risk assessment and willingness to pay to
avoid such risk. In our context, given that a massive earthquake can have a potentially6catastrophic impact on each household, and that the risk is generally fairly differentiated acrossregions, it is quite natural to expect that households will incorporate earthquake risk into theirdecision on choice of location. Rational households are willing to pay an additional amount forhouses located in an area where the probability of hazard is less. Conversely, rational
households may choose to locate within quake-prone areas only if they are compensated fortheir risk in terms of discounted housing prices that can offset the potential loss and damage
from a major seismic event.
Let us assume that there are two states, 0 and 1, corresponding to a “no earthquake” and“earthquake” situation. Let  be the probability of earthquake occurrence (i.e., the probability
assigned to state 1). Hedonic pricing method suggests that housing cost can be described as afunction of both housing/structural characteristics and location-specific characteristics (such asenvironmental factors and neighborhood quality). In our context, differentiated risk ofearthquake will be a particularly important factor influencing the market cost of housing. Hence,
the hedonic price function can be written as:p  ph,  ,

(1)

where p is the observed housing cost (e.g., rental price or sales price of a particular housing
unit), and h is a vector of housing and location-specific characteristics that are not related toearthquake risk.
A household maximizes its expected utility by choosing a bundle of housing characteristics
and level of earthquake risk.2 Let u h, x  be the utility function, where x is an amount of 0 and u  0 . Let
numeraire goods consumption, with u h x

y 0 and y 1 be thestate-contingent income where we assume y 0  y 1 with L  y 0  y 1 being the monetary2

The preferred level of earthquake risk can be achieved by choosing a particular location of residence orby choosing levels of household expenditure on self-protection (Ehrlich and Becker, 1972).

7

loss from the earthquake. Then the household’s budget constraint suggests that the numeraire
goods consumption under two states can be written as

x 0  y 0  ph,   andx1  y1  ph,   , respectively. The household’s expected utility to be maximized can be
represented as:3









max EU  U h, y1  ph,    1   U h, y 0  ph,   .
h ,

(2)

The first-order conditions give us the equilibrium conditions required for optimal levels of
the j th housing/location-specific characteristics and earthquake risk.
10p U h j  1   U h j 
ph j 
0,
h j
U 1x  1   U x0 

p
U 1 U 0
p 

 0,
 U 1x  1   U x0

(3)

(4)

where U 1 and U 0 , respectively, represent the utility when the earthquake occurs and when it
does not, and subscripts denote partial derivatives. Equation (3) indicates that the implicit pricefor any housing/location characteristics reflects the expected (dis-) amenity value. Equation (4)indicates that the implicit price for the probability of earthquake occurrence reflects a utility
difference across states, U 1  U 0 . Since the right-hand-side of both equations (3) and (4) are
divided by the expected marginal utility of numeraire goods consumption, U 1x  1   U x0 ,these implicit price estimates provide a convenient way to evaluate the marginal willingness topay (MWP) in this simplified situation.

3

Alternatively, Kask and Maani (1992) assume that households will optimally choose the level ofself-protection expenditure that reduces the risk of damage from an earthquake event. The different
specification of the model, however, does not change our main theoretical result.

8

The effect of earthquake insurance
In the simplified situation above, the housing market will completely respond to the potential
risk of earthquakes, and any differentiated earthquake risk will be capitalized into differentiatedhousing costs across regions. However, in reality, households can hedge against earthquake risk
through the purchase of insurance policies. In order to examine the effect of the insurance
market, we assume, in a simplified way, that earthquake insurance offers a contract with aknown amount of insurance claims paid when state 1 occurs m  for the annual insurance
premium r   that can vary across regions depending on earthquake probability. In this case,equation (4) can be rewritten as:

U 1 U 0p  r ,
U 1x  1   U x0(5)

where r is the marginal increase in an insurance premium.
Equation (5) has at least two important implications for our benchmark result. First, inequation (5), the implicit price for the probability of earthquake occurrence  p  is no longer
equal to the household’s MWP as in equation (4). This is because regional earthquake risks are
partly reflected in the differentiated premiums in the insurance market. Since we expect that thetrue MWP (i.e., the first term on the right-hand-side) should be negative, and that the insurance
premium is generally higher in risky regions (i.e., r  0 ), the implicit price for earthquake
probability would overestimate the true MWP. In all, the estimates for household’s MWP should
be given by p  r . Secondly, if the claim paid by the insurer is perceived to be equal to theloss from the earthquake m  L  , then income, and thus utility level, in the two states will beequal (i.e., U 1  U 0 ). Under these special conditions, equation (5) suggests that the implicit
price estimate should be equal to the negative of the premium increment, p  r . Several
studies utilize this relationship to test the expected utility theory (MacDonald et al., 1987; Bin

9and Polasky, 2004; Bin et al., 2008). If, instead, the claim paid by the insurer is perceived to beless than the losses caused by the earthquake, then the household’s MWP would be negativeU  U , and the expected relationship would be p  r . In this case, p  r is the
1

0









uninsured component of earthquake risk, i.e., the household’s MWP under partial coverage
insurance.
In Japan, as in any other countries, earthquake damage is nowhere near fully insured, and isheavily loaded reflecting the systematic nature of the risk associated with a major earthquake
close to Shizuoka or Aichi prefectures.4 In addition, ex-post Japanese government grants forvictims may create an immense moral hazard problem in the Japanese insurance market (charity
hazard).

Subjective risk assessment and the effect of information
In the hedonic literature, concern focuses on the risk variable,  , which ideally should
represent subjective risk perceptions rather than objective risk measures (Kniesner, Viscusi,
Woock, and Ziliak, 2007). If consumer’s subjective risk perceptions are systematically different
from objective measures, empirical analysis based only on the objective measures will lead tofaulty results. The maintaining assumption in almost all previous studies is that the subjective
risk assessments can be proxied by objective measures of risk.5 As indicated by Maani andKask (1991) and Kask and Maani (1992), however, the discrepancy between subjective risk

4

In Japan, the premiums are based on the estimates of the likelihood of occurrence and the expected

damages of an earthquake. These estimates are computed at the geographical level of prefectures, andeach prefecture is classified into one of the four rating zones (rank 1 (safest) to rank 4 (riskiest)). The
premiums are based on the government’s regulations, not on private sector calculations.
5

Although in somewhat different context, Viscusi and Aldy (2003) show that published rates of

workplace injury/death are strongly correlated with workers’ subjective risk perceptions.

10perceptions and objective risk measures will lead to biased estimates of a household’s MWP insome cases. Following their approach, we specify the household’s subjective probability,  s ,as a function of the objective probability,  o , and the external information, I . Equation (6)gives the subjective probability function: s  f  o , I .

(6)

Suppose that the market hedonic price function (equation (1)) depends on the objective

probability, p  p h,  o , but households behave according to their subjective risk perception
given as equation (6). Then, for each household, the observable relationship between housing

 



cost and subjective probability would be p  p h, g  s , I , where g  is the inverse of thesubjective probability function. In this case, equation (4) can be rewritten as:p s 

U 1 U 0.
 sU 1x  1   s U x0

Equation (7) indicates that the implicit price for the subjective probability(7) p  would
sprovide the true MWP estimate. The only difference from equation (4) is that we now have theimplicit price for the subjective, rather than objective, probability on the left-hand-side of theequation. Equation (7) also indicates that the implicit price for the subjective probability

p 
s

and the resulting MWP generally depend on the level of external information I , unless
information does not have any influence on the household’s subjective risk assessment, f I  0 .
Hence we can examine modifications of households’ risk perceptions by looking at the implicit
price estimates under different information levels. In the following analysis, we will consider
the occurrence of massive earthquakes as a major source of external information that influences
households’ subjective risk perceptions.
However, the above argument hinges solely on the fact that we can observe a household’s
subjective perceptions rather than objective measures. It can be shown that p s  p o  g  s
11

from the relationship between housing cost and subjective probability. Therefore, due to theexistence of g  s , the implicit price in terms of the objective probability, p o , can be different
from p s , and would not provide the true MWP estimate as in equation (4). The multiplierg  s is sometimes referred to as the transformation bias (Kask and Maani, 1992). In thefollowing analysis, we use the appropriate instrumental variables estimator to empirically copewith this problem. If we can successfully eliminate the transformation bias from our estimates,changes in the hedonic implicit price would solely reflect changes in the household’s perceptionof earthquake risk.

4. Data and Methodology
Data
The Keio Household Panel Survey (KHPS), sponsored by the Ministry of Education, Culture,
Sports, Science and Technology, is the first comprehensive panel survey of households in Japan,
conducted annually by Keio University since 2004. In the first wave, self-administeredquestionnaires were given to 4,005 respondents, male and female, aged 20-69 years. These
respondents were selected by stratified two-stage random sampling.
In the following analysis, four waves of the KHPS (2004–2007) are utilized to examine therelationship between earthquake risk and housing prices in Japan. The KHPS covers both rental
households and homeowners. For rental households, actual monthly rents paid are documented.
For homeowners, owner-provided, self-assessed values of owner-occupied housing are
documented.6 The KHPS also provides detailed information on the type of housing – ownership
6This measure of housing price is constructed from the question about subjective assessment of the value

12status (owned, private rental, or public rental) and construction type (wooden or reinforced
concrete building). Since the anti-seismic quality of the dwelling unit largely depends on
housing type, this information is necessary for evaluating the impact of seismic risk on the
housing market, which is impossible in previous studies due to data limitations.
The earthquake risk measure is taken from the Probabilistic Seismic Hazard Map (PSHM)
provided by the National Research Institute for Earth Science and Disaster Prevention (NIED).7
The PSHM data provides the occurrence probability of earthquakes with a given seismic
intensity at a fairly disaggregated geographical level (1km  1km grid cells).8 In the following
analysis, we use the occurrence probability of earthquakes with ground motions equal to or
larger than JMA seismic intensity 6– as our risk measure.9 Since the original PSHM data
provides the probability of occurrence within 30 years, we calculate the annualized version of

this as 1  1   *

1 / 30, where  * is the original 30-years probability from the PSHM data. An

example of PSHM is shown in Figure 1.
(Figure 1 around here)of current residence (“How much do you think this lot/house would sell for on today’s market?”).
7

The original data is available at http://www.j-shis.bosai.go.jp/.

8

The original PSHM data is provided as the ESRI grid format, where grid cells are defined as geographic

space of equally sized square grid points. The PSHM data gives the earthquake probabilities for every
1km  1km grid cells all over Japan.
9

The Japan Meteorological Agency (JMA) seismic intensity scale, which is measured with a seismic

intensity meter, and is graded from 0 to 7, provides a measure of the strength of seismic motion. The
typical situations and damages caused by the earthquake with JMA seismic intensity 6– are as follows:
People have difficulty standing, wooden houses occasionally collapse, and walls and pillars may bedamaged even in highly earthquake-resistant houses. For full explanation of the JMA seismic intensity
scale, see http://www.jma.go.jp/jma/kishou/know/shindo/explane.html. In general, the relationshipbetween the JMA scale and the Richter scale basically depends on the distance from the epicenter. Evenan earthquake with a small intensity on the Richter scale can have a large JMA intensity at locations near
the epicenter.

13

Since the unit of observation in the original PSHM is defined based on the 3rd level meshcodes (1km  1km grid cells), we aggregate the original data and construct the city-levelprobabilities in order to match the earthquake risk measure with the KHPS.10 The resulting
earthquake risk variables are quite heterogeneous across prefectures; there are remarkably highearthquake probabilities in the northern and southern coastal regions of Honshu Island wheresome of Japan’s largest cities are located (Figure 2).
(Figure 2 around here)
As mentioned above, earthquake risk variables should ideally reflect subjective perceptions
rather than objective assessments. As in previous studies, we do not have information on
subjective risk perceptions, but in some respects our objective estimates of earthquake
probability provide a better way to disentangle price-risk tradeoffs in the housing market. First,
our objective estimates are defined at the fairly disaggregated geographical level, which
substantially reduces the measurement error in the earthquake risk variable. As shown in Figure
1, earthquake probabilities are highly differentiated even within the same prefecture. Therefore,
our objective estimates provide a much more accurate measure of the risk associated with aparticular location than a geographically broader index, such as insurance market-based hazard
zones. Secondly, our risk variable varies by year as well as geographic location.11 While mostprevious studies use time-invariant risk measures or are based on cross-sectional analysis,time-series variation in the risk variable allows us to exploit the potential of panel data and

10This is because, in the KHPS, the information about the respondent’s location of residence is reported

at the city-level. In the following analysis we use the average probability within the city/town as ourmeasure of earthquake risk. We also check several other specifications of the variable (maximum,
minimum etc.), which yield virtually the same results. The city-level probabilities are calculated by
ArcView 9.0.
11

The PSHM is updated annually based on the status of volcanic activity or detection of new active

faults. The updated earthquake occurrence probabilities are made available every January.

14

related estimation techniques.
Our primary purpose is to investigate whether individuals alter their subjective assessmentsof earthquake risk after an earthquake. The occurrence of massive earthquakes is therefore
thought of as a major source of external information that influences household’s subjective risk
perceptions.
As for this information, data is taken from the database of the Japan Meteorological
Agency.12 It gives the date, epicenter, seismic intensity scale, and magnitude of noticeable
tremors from 1926 onward. We create the prefecture-level dummy variable indicating that there
were earthquake events with JMA seismic intensity  6  in the previous year.
We find that, although there are several massive earthquakes during our sample period,
none of the cities/towns in which our sample households are located was actually hit by anearthquake with seismic intensity larger than 6  . Rather, they have witnessed the earthquakes
and resulting damages in their neighboring cities and towns in the same prefecture. Therefore,
the earthquakes under consideration are all in the category of a “near-miss,” informing ourhouseholds about the dangers without directly affecting them. As a result, we do not need tocontrol for any repairs and reconstruction after earthquake damage that might affect housing
rent and property values.

Empirical Model
Our primary interest is in estimating the hedonic implicit price of earthquake risk and itschanges after massive earthquakes. The full specification of the hedonic regression model isgiven as follows:ln  pit     PRit  EQit  PRit  X it   i  t   it ,12

The original data is available at http://www.seisvol.kishou.go.jp/eq/shindo_db/shindo_index.html/.

15

(8)

where p it is the appropriate housing price measure for household i in time t (it will be
actual rent or owner-provided house value, depending on the model to be estimated), PRit isthe earthquake risk variable (i.e., PSHM objective probability measure), EQit is thepost-quake dummy indicating that a massive earthquake (seismic intensity  6  ) occurred in
the previous year, X it is the relevant set of explanatory variables,  i and  t are individual
heterogeneity and time trends, respectively, and  ,  ,  , and  are parameters to be
estimated. Following the previous studies on hedonic analysis of the housing market, X it
includes basic housing characteristics such as number of rooms, floor and garden space, years
since the unit was built, and the time distance to the nearest station/bus stop. We also control for
construction type of the dwelling, city size, and prefecture in which the unit is located. In
addition, since we use self-assessed value as owner-occupied housing price, various respondent
characteristics are also included. These characteristics are: age, age squared, sex, marital status,
education, employment status (private not-for-profit, private for-profit, public/government
sector, self-employed, and not employed), and annual earnings.13 The definition and summary
statistics of the variables are shown in Table 1.
(Table 1 around here)
Our primary interest is in the estimate of  in equation (8), which is the post-quake
change in the effect of earthquake risk on housing prices. Since the overall effect of theearthquake risk variable on housing prices is expected to be negative, a negative estimate on 
indicates that a massive earthquake and resulting damages in the prefecture increases residents’awareness of earthquake risks.
In equation (8), the household fixed effect  i is particularly important for two reasons.
First, households living in quake-prone areas are likely to have “self-protection” for their
13

Since respondent’s sex and education are time-invariant characteristics, they are not included in somemodels with fixed effects specifications.

16

dwellings, e.g., seismic retrofitting. Since we cannot observe the quake-resistance quality of thedwelling and such a characteristic should be included in housing prices, ignoring“self-protection” behavior could cause an underestimation of the effect of earthquake risk. In thefollowing analysis, we assume that the quake-resistance quality of the dwelling is constantacross time and can be captured by  i in our model.14 Second, the heterogeneity of attitudes
toward risk might be another problem. Less risk-averse households tend to live in quake-prone
areas. At the same time, it is also likely that these households will live in areas that have otherunobservable risky characteristics (e.g., flood hazard). In such cases, the estimated coefficientsof the model without the household fixed effect would be biased.
The possibility of another source of bias in our implicit price estimate stems from thediscrepancy between subjective and objective assessments of earthquake risk. Following therecent study by Kniesner et al. (2007), we check this problem by using the familiar
measurement error framework. Let PRito be the objective estimate of earthquake probability
and PRits be the subjective risk perception. We assume that the relationship between objective
probability and subjective risk perception can be represented as:PRits  PRito   it ,(9)where  it represents measurement error pertaining to the household’s perception toward
objective risk. As shown in Section 3, the existence of the transformation bias, i.e., g  s  1 ,would result in faulty MWP estimate using the objective probability measure. If there is no14

We do not distinguish between household and housing unit fixed effects here. Strictly speaking, sincesome households changed their residence during our sample period, these two effects are not exactly thesame. In consideration of this point, we also estimated the model using a restricted sample of non-movers.
However, the result does not change qualitatively.

17

measurement error (i.e., vit  0 ), equation (9) immediately suggests that each household has“correct” risk assessment, and hence there is no transformation bias, g  s  1.
Instead, if there are certain discrepancies between objective probability and subjective risk
assessment as in equation (9), the implicit price in terms of objective probability might bebiased. Since our final goal is to obtain the true MWP estimate in equation (7), i.e., the implicit
price for the subjective perception, the underlying model can be written as:ln  pit     PRits  EQit  PRits  X it  i  t   it .

(10)

If we can observe the subjective risk measure PRits , equation (10) gives the implicit price forthe subjective risk perception, and therefore the true MWP estimate given in equation (7).
However, since we do not have any direct measure for PRits , equation (10) cannot be estimatedin general. Instead, consider the regression of ln  pit  on the observed PRito . By substituting
equation (9) into equation (10), we have









ln  pit      PRito   it  EQit  PRito   it  X it  i  t   it
   PR  EQit  PR  X it  i  t   it ,
o
it

o
it

(11)

where  it is the combined error term equal to  it   it   EQit  it . Because PRito
equals PRits  vit , the regressor in equation (11) is correlated with the combined error term:

Cov[ PRito ,  it ]  0. Hence the OLS estimates of equation (11) with observed PSHM (objective)
probability become inconsistent and we need to use the instrumental variables method.
In this case, in order to obtain consistent estimates, we need to have valid instruments thatare: (i) sufficiently correlated with objective probability PRito , while (ii) independent of themeasurement error  it . Now suppose that the neighbor of household i , say household k , has
18

a subjective assessment of PR kts . Then, equation (9) suggests that we have PRkts  PRkto   kt
for this household. If the measurement error for each household is purely idiosyncratic, then we
expect that household k’s measurement error  kt , and thus PR kto , will be independent ofhousehold i’s  it .15 This allows us to use neighborhood objective probability as the instrumentfor PRito . In the following analysis, we will use within-city variation of the objective
probability as our instrument, rather than picking up a single neighborhood probability
associated with a particular location.

5. Empirical Results
In the following analyses, we estimate separate hedonic regressions for renter households andhomeowners.16 Corresponding dependent variables of these two regressions are the logarithms
of monthly rents and owner-provided house values.17 We first estimate the model without theinteraction term EQit  PRit  to get our baseline result, and then estimate the full model
given in equation (8).

15

One may think that people in nearby locations may have similar components in their measurement

errors due to unobserved location characteristics or some form of residential sorting. But if suchcomponents are time-invariant, within-difference transformation of our hedonic regression can solve thisproblem.
16

Since our analysis is the first study to focus on both homeowners and renters’ response to massive

earthquakes in one paper, we cannot expect that both renters and owners react similarly based on previous
research. This is the reason why we separately estimated two equations for renters and owners.
17

As Kiel and Zabel (1999) and Freeman (1979) have shown, the use of the owners’ valuations will

result in accurate estimates of house price indexes and will provide reliable estimates of the implicit
prices of housing and neighborhood characteristics.

19

Baseline Results
Our baseline result is shown in Table 2. Here we do not distinguish between observations before
and after the massive earthquakes. For both renter households and homeowners, we estimate themodel with OLS and fixed effects. Models [1] and [3] are OLS results without considering
household fixed effects  i . Models [2] and [4] are fixed effects results. Respondent
characteristics, and dummy variables for the dwelling types, prefectures and city-sizes are also
controlled, but omitted from the results. We have included the time dummy variables to capture
the unobserved time-varying effects that change year by year such as a housing market changes
after the earthquake.
(Table 2 around here)
For renter households (Model [1]), the OLS results indicate that earthquake probability has
a significantly negative effect on housing rents. This is consistent with previous studies (Naoi,
Sumita, and Seko, 2007; Nakagawa et al., 2007). Based on the fixed effect estimates (Model[2]), the coefficient of earthquake occurrence probability is still negative, but not significant.
Our interpretation here is that the OLS estimate may pick up unobservable household
heterogeneity or some location-specific characteristics. For example, risk-averse renterhouseholds may choose better houses that are located in an area with good environmental
characteristics (such as low earthquake probability, good schools or transportation links, parksor other amenities, etc.). Without controlling for fixed effects, such an unobserved relationshipmay yield a spurious negative correlation between housing prices and earthquake probability.
Statistically, the F test for the fixed effects model significantly rejects the null hypothesis of no
individual effects ( PrF 830,1223  15.97  0.000 ).
For homeowners (Models [3] and [4]), the negative effect of earthquake probability
becomes larger and significant after controlling for individual fixed effects. It is well-known
20

that the fixed effects model could exacerbate attenuation bias due to measurement errors relative
to OLS if the autocorrelation in unobserved subjective perceptions is large enough relative tothat in measurement errors (Wooldridge, 2002, p.311).18 Hence we expect that the measurement
error problems do not play a dominant role in explaining the changes in implicit price estimates
between these two models. Our intuition here is that extensive “self-protection” behavior among
homeowners, which is not likely to be the case for renters, underestimates the coefficient in
OLS result.
For other independent variables, the results are intuitively plausible. Age of housing andminutes from the nearest station/bus stop both have negative and significant signs except for thelatter in Model [4]. Number of rooms, which is used to measure the scale of housing, has asignificantly positive impact except for Model [2]. Barrier-free equipment has positive signs inhomeowner results (Model [3]). As for the renter sample, this variable has the completely
opposite sign without controlling for fixed effects (Model [1]), but it turns out to have a positiveeffect in the fixed effects model (Model [3]).

Effects of Earthquake Risk Before and After Massive Earthquakes
The main results of our hedonic regressions are shown in Table 3. Specification of the models
shown in Table 3 is identical to those in Table 2 with one exception; we added an interaction
term between the earthquake probability variable and a post-quake dummy variable. 19 A
18

Strictly speaking, the argument can only be applicable to the fixed effect first-difference (FD) estimator,

while our estimation method here is a standard within-difference estimator. With a longitudinal data of T
> 2, estimates from these two fixed-effects estimators can be different. So we have also estimated theModel [2] with FD method, and find that this yields an even larger effect of earthquake probability.
19

If there is market segmentation, the hedonic price function estimated for large areas as a whole will

provide faulty estimates of the implicit prices. To see whether this is the case, we also estimate the modelwith a selected subset of our sample (i.e., households in northern Japan). The estimation with the21

post-quake dummy variable equals one if the earthquake event occurred in the previous year andzero otherwise.20(Table 3 around here)
It is found that post-quake changes in the effect of earthquake risk probability (i.e.,
coefficients on an interaction term,  ) are significantly negative for both models. This suggeststhat massive quakes in neighboring cities/towns changed the perception of earthquake risk forboth renter households and homeowners. Our results indicate that, in the post-quake period, a
0.2 percent increase in the annual earthquake probability, which is nearly 1/3 of the average
probability, leads to a 10,000 yen discount in monthly rents and a 3.8 million yen discount inhousing values. These numbers are approximately 16% of average rent and 13% of housing
value. Moreover, our results also indicate that the pre-quake coefficients of the earthquake risk
variable are not significant in both models. Combined with the significantly negative
coefficients of the interaction terms, we believe that the most plausible interpretation for thisresult is that households are initially unaware of, or at least underestimate, the earthquake risk inthe pre-quake period. The perception of earthquake risk for both renter households andhomeowners, however, changes dramatically following a massive quake in neighboring
cities/towns.21
restricted sample, however, shows qualitatively similar results.
20

We also introduce a post-quake dummy variable itself as an additional explanatory variable.

21

An anonymous referee kindly suggested another interpretation for the insignificant coefficientestimates of the earthquake risk variable in both the rent and house price models as follows. With any
anti-seismic construction, the value of the property before the occurrence of the earthquake will increase
compared to the value of the property without any anti-seismic construction. In this sense, anti-seismic
construction has a direct effect on losses and thus on the value of the property. However, anti-seismic
construction may also lower the risk of a loss. Because of the latter effect, as a result, the coefficientestimates of the earthquake risk variable in both the rent and house price models become insignificant.
Such a story may be plausible if anti-seismic construction is prevalent in the housing market. In Japan,
however, the overall percentage of dwellings with anti-seismic construction features exceeding the22

Several Robustness Checks
As we discussed in Section 3, the implicit price in terms of objective probability might bebiased if there are certain discrepancies between objective and subjective risk assessments. In
order to empirically check this problem, we utilize a familiar measurement error framework andre-estimate the model using the fixed effects instrumental variables estimator for objective
probability (see Section 4 for detail). The instruments we used are the standard deviation ofearthquake probabilities at the city/town-level (within-city S.D. of earthquake probability) and
its interaction with the post-quake dummy variable. As explained before, the rationale for thischoice is that, once household fixed effects are controlled for, a particular household’s
measurement (or perception) error is likely to be uncorrelated with its neighbor’s. Hence thisvariable should correlate with the objective earthquake occurrence probability, but isuncorrelated with the measurement error itself.
These estimation results are summarized in Table 4. The set of explanatory variables usedin the model is identical to that of Table 3. In this model, in contrast, the earthquake occurrence
probability and its interaction with the post-quake dummy are instrumented. Various housing
characteristics and other control variables are also included in the model, but are omitted fromthe results.22(Table 4 around here)

regulatory minimum is less than 3% (Housing and Land Survey, 2003).
22

Housing characteristics included are as follows: age of the dwelling (years since built), time-distancefrom the nearest public transportation, number of rooms, floor in which the room is located (forcondominium units), whether the unit has a garden (for owner-occupied houses), whether the unit has any
barrier-free equipment, and dummies for the type of dwelling. The complete results are available uponrequest.

23

The results of the first stage regressions are shown in the bottom part of Table 4. It is foundthat the within-city S.D. of the earthquake probability is significantly and negatively correlatedwith the average probability for both renter and homeowner samples. The interaction term of thewithin-city S.D. is also shown to be a highly significant predictor of the instrumented
interactions for both models. These results indicate that our instrumental variables havesufficiently strong correlation with earthquake occurrence probability and its interaction withthe post-quake dummy variable.
The second stage regression results related to earthquake occurrence probability are shownin the upper part of Table 4. Again, the post-quake changes in the effect of earthquake
probability are significantly negative for both models. Moreover, even after controlling formeasurement errors, estimated post-quake coefficients and the implicit price estimates are
virtually identical to those presented in Table 3. To see whether measurement error problems
yield significantly different parameter estimates, we test the equivalence of the results in Tables
3 and 4 by Hausman specification tests. For the renter sample, the test statistic of 1.78, which
follows  2 36 under the null hypothesis, is small enough to accept the null hypothesis. For
the homeowner sample, the test statistic of 2.10, which follows  2 22 , is also small enoughto accept the null. Hence we can say that our main results using objective PSHM probability as
a proxy for subjective risk assessment provide sufficiently reliable implicit price estimates.
As well as the measurement error issue, the main results in Table 3 rely on the assumptionthat our post-quake dummy variable credibly picks up purely informational effects of theearthquake occurrence. Because a massive earthquake will have a devastating impact on thelocal housing market, it may affect housing prices through changes in overall demand andsupply conditions.23 We have already shown in Table 3 that the post-quake dummy variable
23

Kawawaki and Ota (1996) investigated the influence of the Great Hanshin-Awaji (Kobe) Earthquake

24

itself does not have any significant effects in hedonic regressions, implying that the actual
earthquakes under consideration do not have any direct impact on the local housing market.24
However, to examine this point in more detail, we added several neighborhood variables into
our regressions. In Table 5, three neighborhood characteristics are added: Population density,
per-capita income, and the number of new housing starts.25 The first two variables are expected
to control for the fact that regions with dense population and/or economic activities might suffermuch more extensive damage from a major earthquake. The number of new housing starts
would control for the changes in demand and supply in the local housing market due to temporalshocks after a massive earthquake.
Even after controlling for these possible neighborhood effects of a massive earthquake, ourpre- and post-quake implicit price estimates are still comparable with those in Tables 3 and 4. In
Model [1] for renter households, both pre- and post-quake effects of earthquake occurrence
probability become significantly negative, and higher population density leads to higher rental
prices as expected. In Model [2] for owner households, the post-quake effect of earthquake
probability is still significantly negative with similar magnitude as those shown in the previous
results. The negative coefficient on the number of new housing starts suggests that post-disasteron the housing market in terms of changes in house prices and housing rents and analyzed the mechanismof those changes.
24

An anonymous referee kindly suggested another explanation for the positive (but insignificant)coefficient estimates of the post-quake dummy variable. If stringent anti-seismic construction codes wereadopted following the earthquakes, this policy change would lead to costly housing construction, andhence have a positive impact on the price of individual housing units. Following his/her suggestion, we
have checked the recent revision of the anti-seismic construction codes in Japan. We find that there are no
major revisions during our sample period.
25

All three variables are defined at the prefecture-levels and calculated using the following sources.

Population density: “Annual Report on Current Population Estimates” (Statistics Bureau). Per-capita
income: “Annual Report on Prefectural Accounts” (Cabinet Office). Number of new housing starts:“Annual Statistics on Construction” (Ministry of Land, Infrastructure, and Transport).

25

supply increases drive down local housing prices.
(Table 5 around here)

Comparing Property Value Discounts with Capitalized Insurance Premiums
We compare the predicted property value discounts with the present value of earthquake
insurance premiums in Table 6 based on our results in Table 3 to investigate whether
homeowners alter their subjective assessments of earthquake risks after massive earthquakes
under the partial coverage insurance. As discussed in Section 3, if homeowners are
well-informed about earthquake hazards and are fully insured, property value discounts alongwith the increased earthquake risk should be equal to the negative of the premium increment,p  r . In reality, however, since earthquake insurance only covers damages and losses of
housing assets and family items, earthquake risk has some components, such as injury, death,and some non-monetary losses, that cannot be insured. Hence we expect that housing pricereductions might exceed the cost of earthquake insurance, that is, we expect p  r  0
where p  0 and r  0 . The purpose of Table 6 is to examine whether p  r , theuninsured component of earthquake risk (i.e., the household’s true MWP under the partial
coverage insurance case), changes after massive earthquakes.
Since most renter households in Japan do not take out earthquake insurance, we primarily
focus on homeowners and owner-provided housing values. For the calculation of earthquake
insurance premiums, we assume earthquake insurance for a house with a value of 30 million
yen.26 Annual insurance premiums are based on NLIRO insurance rates for wooden housing
without any anti-seismic construction method. Since annual insurance premiums basicallydepend on the earthquake hazard of the area in which a house is located, we estimate the26

The average value of the house in our sample is about 30 million yen.

26

premiums for four risk categories (Rank 1 (safest zone) – Rank 4 (riskiest zone)). The annual
insurance payments are discounted using discount rates of 3% (column 2) and 5% (column3).
For the calculation of property value differentials, we use the housing value discounts froma hypothetical riskless situation (i.e., earthquake probability = 0%). Based on the observed
average earthquake probability for each risk category (column 4), predicted house values arecalculated and the property value discounts are obtained. Property value discounts are defined as
the predicted property value under a riskless situation minus that under an actual earthquake
probability. Hence the property value discounts presented here can be interpreted as anempirical counterpart of the implicit price for the earthquake probability in absolute terms, i.e.,p . We calculated these property value differentials for both before and after the earthquake
event (columns 5 and 6).
(Table 6 around here)
When we look at the insurance premiums under a 3% discount rate, property value
differentials for the pre-quake period are smaller than insurance premiums except for risk
ranking 4, which means p  r and hence p  r  0 . Given that the Japanese insurance
system does not provide full coverage, this is counterintuitive since, if p  r  0 , equation(7) implies that the consumer has higher utility when the earthquake occurs. A possible
interpretation is that pre-quake assessments of p are underestimated, i.e., homeowners are
initially in denial about earthquake risk. Assuming a 5% discount, we can say the same thing
except for risk rankings 3 and 4. The post-quake discounts, on the other hand, generally exceed
the capitalized insurance costs, i.e., p  r  0 . These results are consistent with above
interpretation that homeowners initially underestimate the earthquake risk before an actual
earthquake occurs. Further, if the post-quake assessment of earthquake risk is more accurate27

than pre-quake assessments, the difference between post-quake property value discounts and thepresent value of insurance premiums represents the uninsurable cost of earthquakes.

6. Conclusion
The purpose of this paper is to examine whether individuals alter their subjective assessments ofearthquake risks after massive earthquake events. We use nation-wide household longitudinal
data coupled with earthquake hazard information and the observed earthquake record to
estimate individuals’ valuation of earthquake risk. The earthquake risk premium is estimatedusing hedonic price models for housing rents and owner-provided house values. Using a
difference-in-differences (DID) approach with longitudinal data, we find that there are somemodifications of individuals’ assessments of earthquake risk following a major tectonic event.
Our results for homeowners suggest that the post-quake discounts for property values withinquake-prone areas more than doubled compared with pre-quake values. We argue that the most
likely interpretation for this result is that homeowners initially underestimate earthquake risk.
The policy implication of our results is clear. If the government properly assesses
earthquake risk and this assessment is widely disseminated among the public, occurrence ofearthquake events would not substantially alter individuals’ perception toward risk. In this paper,
however, we find evidence of significant changes in risk assessment resulting from the actual
experience of massive earthquakes. Our results indicate that there is much room for
improvement in current anti-seismic disaster policies regarding public perceptions of potential
earthquake losses.27

27

Quigley and Rosenthal (2008) show the importance of a diverse treatment of the economic and public

aspects of urban disasters. Troy and Romm (2006) assess the effects of hazard disclosure on housing
prices in statutory flood- and fire-hazard zones and analyze whether those effects were conditioned by28

Improved risk assessment depends on the government’s extensive educational campaigns
and greater transparency concerning the government’s local risk assessments. In addition, theearthquake insurance system should be modified by the government to reflect more precise risk
assessment. If the earthquake insurance system truly reflects more precise earthquake risk
assessment, and the results are widely disseminated, households are more likely to recognize thetrue extent of earthquake risk in their own areas and act accordingly.28 In order to increase
consumers’ awareness of natural hazard risk, introduction of a law for housing lenders, sellers
and real estate agents requiring disclosure of natural hazard risk, such as the 1998 California
Natural Hazard Disclosure Law (AB 1195)29, might be an effective policy. It is important toremind households to always properly assess earthquake risk even if they have neverexperienced massive earthquakes in their areas, and these reminders are most likely to beeffective at the point of sale, i.e. real estate brokers and housing lenders. Our results suggest that,prior to massive earthquakes, homeowners tend to underestimate earthquake risk, or are totallyunaware that they live in a quake-prone area, and thus do not adopt adequate anti-seismic
measures or purchase insurance policies. This lack of awareness justifies some form ofgovernment intervention regarding anti-seismic policies that help people better assess andaddress their risk.
The government should devise anti-seismic policies reflecting accurate earthquake risk
assessment in each area. For example, the government should impose strict anti-seismicbuilding codes for buildings in risky areas and encourage households to modify their dwellings
to lessen seismic risk. Targeted subsidies and tax deductions aimed at promoting seismic risk

race/ethnicity, income, and previous occurrence of hazards in those zones.
28

Kunreuther (2008) examined the role that insurance and mitigation can play in reducing losses fromnatural disasters.
29

For the details of the AB1195, see Troy and Romm (2006).

29

counter-measures could nudge households to upgrade their dwellings. The government could
also impose risk premiums on businesses and target tax deductions and subsidies to encouragecompanies to locate offices and factories in less earthquake prone areas as a way of shifting
population away from riskier areas. The haunting images from Sichuan are compelling reasonsfor adopting more proactive policies in Japan.

References
Beron, K.J., J.C. Murdoch, M.A. Thayer, and W.P.M. Vijverberg (1997), “An Analysis of the
Housing Market before and after the 1989 Loma Prieta Earthquake,” Land Economics,
73(1), pp.101–113.
Bin, O., and S. Polasky (2004), “Effects of Flood Hazards on Property Values: Evidence Before
and After Hurricane Floyd,” Land Economics, 80(4), pp.490–500.
Bin, O., J.B. Kruse and C.E. Landry (2008), “Flood Hazards, Insurance Rates, and Amenities:
Evidence from the Coastal Housing Market.” Journal of Risk and Insurance, 75(1),pp.63–82.
Brookshire, D.S., M.A. Thayer, J. Tschirhart, and W.D. Schulze (1985), “A Test of the Expected
Utility Model: Evidence from Earthquake Risks,” Journal of Political Economy, 93(2),pp.369–389.
Ehrlich, I. and G.S. Becker (1972), “Market Insurance, Self-Insurance, and Self-Protection,”
Journal of Political Economy, 80, pp.623–648.
Freeman M. (1979), “Hedonic Prices, Property Values and Measuring Environmental Benefits:
A Survey of the Issues.” Scandinavian Journal of Economics, 81(2), pp.155–173.
Hallstrom, D.G., and V.K. Smith (2005), “Market Responses to Hurricanes,” Journal of30

Environmental Economics and Management, 50(3), pp.541–561.
Kask, S.B. and S.A. Maani (1992),“Uncertainty, Information, and Hedonic Pricing,” Land
Economics, 68(2), pp.170–184.
Kawawaki, Y. and M. Ota (1996), “The Influence of the Great Hanshin-Awaji Earthquake on the
Local Housing Market,” Review of Urban and Development Studies, 8, pp. 220–233.
Kiel, K.A and J.E Zabel (1999), “The Accuracy of Owner-Provided House Values: The
1978-1991 American Housing Survey,” Real Estate Economics, 27(2), pp.263–298.
Kniesner, T.J., W.K. Viscusi, C. Woock and J.P. Ziliak (2007), “Pinning Down the Value of
Statistical Life,” IZA DP No. 3107. Available at http://ssrn.com/abstract=1029912/.
Kunreuther, H. (2008), “Catastrophe Insurance: Challenges for the U.S. and Asia,” Paper
presented at the 2008 ASSA Annual meetings in New Orleans.
Maani, S.A. and S.B. Kask (1991), “Risk and Information: a Hedonic Price Study in the New
Zealand Housing Market,” Economic Record, 67(3), pp.227–236.
MacDonald, D.N., J.C. Murdoch and H.L. White (1987), “Uncertain Hazards, Insurance, andConsumer Choice: Evidence from Housing Market,” Land Economics, 63(4), pp.361–371.
MacDonald, D.N., H.L. White, P.M. Taube, and W.L. Huth (1990), “Flood Hazard Pricing andInsurance Premium Differentials: Evidence from the Housing Market,” Journal of Risk andInsurance, 57(4), pp.654–663.
Nakagawa, M., M. Saito, and H. Yamaga (2009), “Earthquake Risks and Land Prices: Evidence
from the Tokyo Metropolitan Area,” Japanese Economic Review, 60(2), pp.208–222.
Nakagawa, M., M. Saito, and H. Yamaga (2007), “Earthquake Risk and Housing Rents:
Evidence from the Tokyo Metropolitan Area,” Regional Science and Urban Economics,
37(1), pp.87–99.
Naoi, M., K. Sumita, and M. Seko (2007), “Earthquakes and the Quality of Life in Japan,”

31

Journal of Property Research, 24(4), pp.313–334. .
Quigley, J.M. and L. Rosenthal (2008), Risking House and Home: Disasters, Cities, Public
Policy, Berkeley, CA: Berkeley Public Policy Press.
Shilling, J.D., J.D. Benjamin, and C.F. Sirmans (1985), “Adjusting Comparable Sales for
Floodplain Location,” Appraisal Journal, 53(3), pp.429–436.
Troy, A. and J. Romm (2006), “An Assessment of the 1998 California Natural Hazard
Disclosure Law (AB 1195),” Policy Research Program, California Policy Research Center,
University of California.
United Nations Development Programme (2004), Reducing Disaster Risk: A Challenge forDevelopment, New York: John S. Swift Co.
Viscusi, W.K. and J. Aldy (2003), “The Value of a Statistical Life: A Critical Review of Market
Estimates throughout the World,” Journal of Risk and Uncertainty, 27(1), pp.5–76.
Wooldridge, J.M. (2002), Econometric Analysis of Cross-Section and Panel Data, Cambridge,
MA: MIT Press.

32

Figure 1: Example of the Prababilistic Seismic Hazard Map (PSHM)

Earthquake Probability(JMA Seismic Intensity Scale ≥ 6 - )- 0.2%
0.2% - 0.5%
0.5% - 1.0%
1.0% - 1.5%
1km
1km

1.5% Source: Japan Seismic Hazard Information Station
http://www.j-shis.bosai.go.jp/

Figure 2: Earthquake Occurrence Probability by Prefecture

Earthquake Probability(JMA Seismic Intensity Scale ≥ 6 - )- 0.2%
0.2% - 0.5%
0.5% - 1.0%
1.0% - 1.5%
1.5% Source: Japan Seismic Hazard Information Station
http://www.j-shis.bosai.go.jp/

Table 1: Variable Definitions and Summary Statistics
SampleRenter Households

Mean
Housing Prices
Housing rent (10,000 yen / month)
Owner-provided value (10,000 yen)
Earthquake Variables
Earthquake occurrence probability (%, annual, JMA intensity ≥ 6–)
Post-quake dummy (= 1 if the earthquake with JMA intensity ≥ 6–
occurred in the previous year)
Housing Characteristics
Age of the building (years since built)
Time-distance from the nearest railway station/bus stop (in min.)Number of rooms
Floor in which the room is locatedHouse with a gardeng
Any barrier-free equipments for elderlyType of dwelling
One-unit building detached from any other buildings
One-unit building attached to one or more buildings
Reinforced concrete building with two or more apartments
Wooden building with two or more apartments
Other type of building
Location Characteristics
14 major cities
Other cities
Towns/villages
N(S.D.)

Homeowners

Mean(S.D.)

6.172(3.267)————2966.5(2648.2)

0.589(0.798)0.640(1.004)0.048(0.214)0.039(0.194)19.171 (12.869)
8.610(6.893)3.381(1.197)1.891(1.973)——0.268(0.443)20.713(14.223)
10.151(9.273)
6.183(1.880)——0.771(0.420)()
0.521(0.500)

0.182
0.047
0.558
0.197
0.016

0.984
0.013
0.002
0.001
0.001

(0.386)(0.211)(0.497)(0.398)(0.125)0.347(0.476)0.571(0.495)0.081(0.273)2,204(0.126)(0.114)(0.039)(0.024)(0.028)0.193(0.395)0.619(0.486)
0.187(0.390)5,169

Table 2: Effects of Earthquake Risk Measures on Housing Prices
Model
Dependent Variable
MethodEarthquake occurrence probability
Age of the building (years since built)
Time-distance from the nearest railway station/bus stop (in min.)Number of rooms
Floor in which the room is locatedHouse with a garden
Any barrier-free equipments for elderly
Respondent Fixed Effect
Dwelling type dummy
Prefecture and city-size dummyYear dummy
N
R2

[1]
[2]
Rent (10,000yen/month)
OLS
FE
Coef.
(S.E.)
Coef.
(S.E.)**-0.1180(0.0240) -0.0390(0.0461)-0.0127 ** (0.0010) -0.0077 ** (0.0021)(0.0023)-0.0069 ** (0.0015) -0.0047 ***(0.0104) 0.0004(0.0151)
0.0572-0.0129(0.0090) -0.0410 *(0.0190)————-0.0840

**

No
Yes
Yes
Yes
2,204
0.3557(0.0264)0.0699

*

Yes
Yes
Yes
Yes
2,204
0.1875(0.0333)[4][3]
Owner-provided value (10,000yen)
OLS
FE
Coef.
(S.E.)
Coef.
(S.E.)-0.0030(0.0162)-0.0829(0.1021)-0.0116 ** (0.0009)-0.0224 ** (0.0036)-0.0070 ** (0.0012)
0.0034(0.0026)***
0.1072(0.0065)0.0392(0.0190)————0.2370 ** (0.0266)0.1872 ** (0.0202)
No
Yes
Yes
Yes
5,169
0.33490.0591
0.0653(0.0577)(0.0423)

Yes
Yes
Yes
Yes
5,178
0.1355

Notes :** *+, , and indicate that the estimated coefficient is significant at the 0.01, 0.05 and 0.10 levels, respectively. Dummy variables for dwelling types, prefectures, city-sizes, and survey years are also controlled but areomitted from the results. Robust standard errors are reported in the parentheses.

Table 3: Effects of Earthquake Risk Measures Before and After Earthquake Events
Model
Dependent Variable
MethodEarthquake occurrence probability
Earthquake occurrence probability x Post-quake dummy
Post-quake dummy
Age of the building (years since built)
Time-distance from the nearest railway station/bus stop (in min.)Number of rooms
Floor in which the room is locatedHouse with a garden
Any barrier-free equipments for elderly
Respondent Fixed Effect
Dwelling type dummy
Prefecture and city-size dummyYear dummy
N
R2

[1]
Rent
FE
Coef.
(S.E.)-0.0382(0.0288)(0.2055)-0.3637 +(0.0245)0.0265**(0.0012)-0.0078-0.0046 ** (0.0012)(0.0104)
0.0004**-0.0402(0.0120)——0.0692

**

Yes
Yes
Yes
Yes
2,204
0.1895(0.0221)[2]
Owner-provided value
FE
Coef.
(S.E.)-0.0849(0.0842)-0.2818 *(0.1428)0.0512(0.0398)**-0.0223(0.0026)
0.0032 +(0.0016)**0.0384(0.0118)——(0.0443)0.0588*0.0665(0.0311)
Yes
Yes
Yes
Yes
5,169
0.1383

Notes :** *+, , and indicate that the estimated coefficient is significant at the 0.01, 0.05 and 0.10 levels, respectively. Dummy variables for dwelling types,
prefectures, city-sizes, and survey years are also controlled but are omitted from the results.

Table 4: Instrumental Variable Estimates of Hedonic Regressions
Model
Dependent Variable
Method

[1]
Rent
FEIV
Coef.

(S.E.)[2]
Owner-provided value
FEIV
Coef.
(S.E.)

Second-Stage Results

Earthquake occurrence probability-0.0857 +(0.0466)(0.2307)
Earthquake occurrence probability x Post-quake dummy-0.4109 +(0.0253)
Post-quake dummy
0.0293First-Stage Results
Dependent Variable: Earthquake occurrence probability
Within-city S.D. of earthquake probability-14.4491 ** (0.5097)
Within-city S.D. of earthquake probability x Post-quake dummy-0.1996(0.9739)
Dependent Variable: Earthquake occurrence probability x Post-quake dummy
Within-city S.D. of earthquake probability
0.0281(0.0411)**5.5739(0.0785)
Within-city S.D. of earthquake probability x Post-quake dummy
Respondent Fixed EffectYes
Dwelling type dummy
Yes
Prefecture and city-size dummy
YesYear dummy
Yes
N
2,204
0.1878R20.1866-0.2526 +0.0476(0.2108)(0.1527)(0.0403)-6.1114 **-0.5203 +(0.2557)(0.3094)-0.0577(0.0574)**
10.2008(0.0695)
Yes
Yes
Yes
Yes
5,169
0.1353

Notes :** *, , and + indicate that the estimated coefficient is significant at the 0.01, 0.05 and 0.10 levels, respectively. Model specifications are same as
those in Table 3. Housing characteristics and dummy variables for dwelling types, prefectures, city-sizes, and survey years are also controlled but
are omitted from the results.

Table 5: Robustness Check - Controlling for Neighborhood Characteristics
Model
Dependent Variable
MethodEarthquake occurrence probability
Earthquake occurrence probability x Post-quake dummy
Post-quake dummy
Neighborhood Characteristics
Population density (1,000person/km2)
Per-capita income (1,000yen)
Number of new housing starts (units/1,000person)
Respondent Fixed Effect
Dwelling type dummy
City-size dummyYear dummy
N
R2

[1]
Rent
FE
(S.E.)(0.0276)(0.2114)(0.0253)[2]
Owner-provided value
FE
Coef.
(S.E.)-0.0740(0.0836)-0.2807 +(0.1429)0.0495(0.0401)

0.1034 ** (0.0261)(0.0001)
0.0002(0.0081)-0.0111
Yes
Yes
Yes
Yes
2,204
0.12970.1061(0.0740)0.0003(0.0003)*-0.0257(0.0130)
Yes
Yes
Yes
Yes
5,169
0.1378Coef.
-0.0677 *-0.3556 +0.0262

Notes :** *, , and + indicate that the estimated coefficient is significant at the 0.01, 0.05 and 0.10 levels, respectively. Model specifications are same as
those in Table 3. Housing characteristics and dummy variables for dwelling types, city-sizes, and survey years are also controlled but are omitted
from the results.

Table 6: Earthquake Insurance Premium and Predicted Property Value Discounts
AnnualInsurance
Premium
Value of Houses = 30 million yen
Rank 1 (Safest Zone)
3.6
Rank 2
5.0
Rank 3
7.1
Rank 4 (Riskiest Zone)
10.7

Present Value of
Insurance Premium underDifferent Discount Rate
3%
5%
123.6
170.0
242.1
365.7

75.6
104.0
148.1
223.7

AnnualEarthquake
Probability

Predicted Property Value
Discounts
Before

0.07%
0.28%
0.72%
1.49%

18.3
71.6
178.3
356.1

After
78.2
297.0
697.3
1261.5

Notes :
Annual earthquake insurance premium is calculated based on the rate for wooden housing provided by the Non-Life Insurance Rating
Organization of Japan (NLIRO). Full coverage insurance for a house with its value 30 million yen is applied. Extra discounts forearthquake-resistant buildings are ignored here. Insurance premiums and property value differentials are in 10,000 yen.